Time-Series Analysis Essay

Time-series data are repeated, regularly spaced measurements over time. Time-series analysis is designed to leverage the longitudinal information contained in such data and involves examining questions about the effect of interventions on the data series and relationships among series. A starting point for time-series analysis is the characterization of the data generating process (DGP). Determining whether the DGP is stationary, fractionally integrated, or integrated is an important first step. If a series is stationary, there is no systematic change in the mean or variance, and no strict per iodic variations. Because most probability theory on time series is concerned with the case of a stationary series, analysis typically requires turning a no stationary time series into a stationary one. An integrated or fractionally integrated series can be made stationary by differencing or fractionally differencing. Fractional integration provides a flexible characterization of the DGP by relaxing knife-edged distinctions between stationary and nonstationary series. Differencing to obtain a stationary series—or fractionally differencing with more precision— avoids spurious regression problems.

Error correction models are a common time-series approach and allow analysts to discuss both short and long-term relationships among series .Vector autoregression is also popular, in particular due to its handling of endogeneity; it is less restrictive than structural equation techniques, as it does not impose exogeneity assumptions. Finally, generalized autoregressive conditional heteroskedastic models are prominent, and can be used to account for heteroskedasticity or volatility in a series.


  1. Box, George E. P., and Gwilym M. Jenkins. Time Series Analysis: Forecasting and Control. San Francisco: Holder Day, 1970.
  2. Box-Steffensmeier, Janet M., John Freeman, and Jon Pevehouse. Time Series Analysis for Social Scientists. Cambridge: Cambridge University Press, 2007.
  3. Enders,Walter. Applied Econometric Time Series, 2nd ed. New York: Wiley, 2004.
  4. Freeman, John R., John T.Williams, and Tse-min Lin. “Vector Autoregression and the Study of Politics.” American Journal of Political Science 33, no. 4 (1989): 842–877.
  5. Ostrom, Charles, and Renee Smith. “Error Correction, Attitude Persistence, and Executive Rewards and Punishments: A Behavioral Theory of Presidential Approval.” Political Analysis 4 (1992): 127–183.

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